Those that do make use of a many-to-many frame architecture include the following: In [1]}, the authors used a vanilla 24-layer CNN to estimate an ideal ratio mask (IRM). A similar idea is explored in [2]}, in which a more sophisticated architecture with batch normalisation [3]} and skip connections [4]} was used to directly estimate LPS features [5]}. EHNet [6]} employed a number of convolutional layers to generate a multiple feature maps, which are concatenated and fed into an RNN layer to better exploit temporal structures. The authors of [7]} adapted U-net [8]} for phase sensitive speech enhancement. They achieved this by constructing layers that are able to handle complex numbers. Finally, we mention [9]}, a GAN-based method.
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